September 2019 | ISE Magazine 33

Reducing surgical residents’ burnout

using neurofeedback

Study measures results of medical workers’

depression before and after treatment

By Lukasz M. Mazur, Alana Campbell, Prithima Mosaly, Karthik Adapa, Dr. Ian Kratzke,

Dr. Lawrence M. Marks, Dr. Samantha Meltzer-Brody and Dr. Timothy M. Farrell

34 ISE Magazine | www.iise.org/ISEmagazine

Reducing surgical residents’ burnout using neurofeedback

In recent years, changes in the healthcare industry have

increased scrutiny on ﬁnancial productivity, quality of

care, patient safety and care outcomes taking away au-

tonomy from clinicians. It is no surprise that national

studies suggest that burnout and depression rates among

surgeons range from 30% to 38% and have increased

over the past ﬁve years to more than 50% (“Multiple Insti-

tution Comparison of Resident and Faculty Perceptions of

Burnout and Depression During Surgical Training,” Michael

L. Williford, Sara Scarlet, Michael O. Meyers, et al, JAMA

Surgery, 2018).

Burnout is a stress-related syndrome and is characterized

by emotional exhaustion, depersonalization and a decreased

sense of personal accomplishment. The prevalence of burn-

out and depression are greater among residents than among

medical students, physicians or college graduates of similar

age. Furthermore, surgical residents with high burnout and

depression are at an increased risk for suicidal ideation.

Training of surgical residents involves complex and de-

manding cognitive activities including multitasking, clinical

reasoning, problem-solving and overall information process-

ing, all of which results in high cognitive workload. Studies

suggest that cognitive workload is impaired in burnout and/

or depressed residents exposed to high task demands. There-

fore, surgical residents with burnout or depression are more

likely to commit medical errors that can lead to patient safety

issues, including patient harm.

To optimize cognitive workload, neurofeedback proto-

cols are being increasingly used in diverse ﬁelds, including

healthcare. Neurofeedback is a scientiﬁcally based technique

that allows the brain to train its self-regulation skills. The

process is based on operant conditioning and it is often de-

scribed as “exercise for the brain” that increases the efﬁcien-

cies of speciﬁc brain functions and enhances cognitive skills.

To date, no previous work has investigated the efﬁcacy

of neurofeedback protocols in improving cognitive work-

load, performance and symptoms of burnout and depression

in surgical residents. We herein present the results of an in-

novative pilot study intended to assess the impact of neuro-

feedback on the cognitive workload of surgery residents with

burnout and depression. Notable improvements in cognitive

workload and growth areas following the neurofeedback

treatment were recorded, suggesting a possible return to less

burnout condition.

Methods used in the study

From June to August 2018, 15 surgical residents with burn-

out – a Maslach Burnout Inventory (MBI) score of more

than 27 – and depression – a Patient Health Questionnaire-9

Depression Screen (PHQ-9) score of more than 10 – from

one academic institution were enrolled and participated in

this institutional review board (IRB) approved prospective

study. Ten residents with more severe burnout and depres-

sion scores were assigned to a neurofeedback treatment, and

ﬁve others were treated as controls.

Each participant’s cognitive workload (or mental effort)

was assessed initially, and again at an eight-week interval, via

electroencephalogram (EEG) with the oscillatory power re-

corded while the subjects performed a computerized n-back

working memory task. This task, a widely used measure for

the assessment of working memory function, involved indi-

cating when a current stimulus (a picture) matched the one

from n steps earlier in a sequence (e.g., “1-n” requires that

participants had to remember the picture presented one im-

age previously, and so on). It used E-Prime software, with

an inter-stimulus interval of 1,500 milliseconds and stimulus

presentation time of 500 ms, while seated in front of a com-

puter in a sound and light attenuated room, at 72 degrees

Fahrenheit.

The treatment consisted of eight validated alpha-theta

neurofeedback sessions, each 35 minutes long, during the

eight-week interval. The alpha-theta protocol was divided

into two separate periods: 1) Pz alpha/theta training period

(eyes closed, deep relaxation for 24 minutes) with the inhibit

frequencies set to 2-4 hertz (Hz) and 15-30 Hz and the re-

ward frequencies set to 5-7 Hz and 8-11 Hz; and 2) C5 beta

awakening/arousal period to ensure proper mental activa-

tion before releasing participating residents back to the clini-

cal work areas (eyes open for nine minutes) with the inhibit

frequencies for beta C5 training set at 1-12 Hz and 20-30

Hz and the reward frequencies set to 15-18 Hz. Throughout

the neurofeedback periods, the impedance was maintained

below 10 kiloohm.

Overall, each session began with instructions for the par-

ticipants to remain relaxed and still for approximately 20 to

40 seconds as BrainPaint neurofeedback software gathered

baseline measures for the reward and inhibit frequencies.

Next, participants were instructed to perform their “best”

to maintain their cognitive state for deep relaxation dur-

ing alpha/theta protocol as guided by the respective reward

frequencies. No speciﬁc instructions were provided for the

awaking/arousal period.

During both periods, when rapid increases in the 1-12 Hz

and 22-30 Hz frequency ranges were 30% greater than the

amplitudes recorded during the baseline period, participants

were notiﬁed by the software via verbal and visual feedback

about potential excessive movement or muscle tension. EEG

recordings were made on a BrainVision Recorder, with

BrainAmp 32 channel system sampled at 500 Hz ﬁltered

online between 0.16 and 100 Hz, then were preprocessed

and analyzed in EEGLAB and with Matlab scripts. All EEG

data were pre-processed and analyzed by an expert cognitive

scientist with speciﬁc expertise in EEG data processing using

custom EEGLAB and Matlab scripts. Data were downsam-

September 2019 | ISE Magazine 35

pled to 256 Hz and independent com-

ponent analysis (ICA) was run to help

identify and remove artifacts (e.g., eye

blinks) and segmented around stimuli

by type from -4 to 4s. Analysis of vari-

ance (ANOVA) with parametric boot-

strapping tested for signiﬁcance between

the degree of change (pre- vs. post-) in

the treatment and control groups. All

P values were two-sided, with P < .05

deemed signiﬁcant.

In addition, to proactively mitigate

study risks, we asked each participant to

report daily on their overall status (e.g.,

amount and quality of sleep, feeling of

being depressed, thoughts of suicide).

Participants used a four-point scale

ranging in general from better than yes-

terday, same as yesterday, average, worse

than yesterday, to one of the worst days

ever. Participants were also asked to

identify three to four speciﬁc growth ar-

eas in which they would like to improve

over the eight-week period (e.g., con-

centration on task, stress levels, temper,

overall energy), using the learnings from

the neurofeedback sessions.

Thus, starting with second session,

participants reported progress using a

-100 to +100 absolute scale for each

selected growth area. For each session,

we averaged these absolute differences

in scores to generate a composite score

reﬂecting the overall perceived progress

by the intervention group on their indi-

vidualized growth areas. With this data,

we analyzed for the correlation between

the time (sessions) and average absolute

improvements in growth areas as report-

ed by the participants.

Results from neurofeedback sessions

Figure 1 demonstrates overall EEG power per time (x-axis)

and frequency (y-axis) for the n-back target stimuli during

the pre- and post-assessments in the control and treatment

groups. Both groups show relatively high cognitive workload

in the pre-assessment, with somewhat inefﬁcient theta (4-8

Hz) and alpha (8-11 Hz) activity (represented by more dark

blue color).

After the neurofeedback intervention, the treatment group

showed a signiﬁcant (p < 0.01) improvement in cognitive

workload during the working memory task with changes

in EEG oscillatory theta and alpha power. These differences

were not noted in the control group.

Throughout the study, we experienced only two instances

when two different participants reported concerning trends

in their feeling of depression and were immediately contacted

by an experienced psychiatrist to intervene as needed. Fortu-

nately, no major interventions were needed, and in both cases

participants were allowed to continue with the study proto-

col. Participants also reported signiﬁcant improvements in the

growth areas – signiﬁcant correlation between time (sessions)

and absolution improvements in growth areas (p < 0.01).

FIGURE 1

Neurofeedback, before and after

Overall EEG power per time (x-axis) and frequency (y-axis) for the n-back target stimuli

during the pre- and post-assessments in the control and treatment groups.

36 ISE Magazine | www.iise.org/ISEmagazine

Reducing surgical residents’ burnout using neurofeedback

In this study, there was a notable change in cognitive

workload following the neurofeedback treatment, suggest-

ing a return to a more efﬁcient neural network. To the best of

our knowledge, this is the ﬁrst study in surgical residents to

demonstrate improvements in cognitive workload as quanti-

ﬁed via brain EEG patterns following a neurofeedback treat-

ment.

In all 15 subjects, initial baseline activity exhibited rela-

tively inefﬁcient cognitive workload, especially as repre-

sented by theta (4-8 Hz) and alpha (8-11 Hz) activity. Such

a pattern, while hypothetical in residents with burnout, is

worrisome and could reﬂect the need to recruit additional

cognitive resources to complete the working memory task.

For example, quantitative EEG studies, while not focus-

ing on cognitive workload, suggest that inefﬁcient theta (4-8

Hz) and alpha (8-11 Hz) activity is associated with post-

traumatic stress disorder and elevated theta is associated with

impaired working memory performance in patients with

PTSD. Empirical evidence from prior randomized control

trials and multiple pilot/exploratory studies demonstrates the

effectiveness of neurofeedback in the treatment of PTSD –

e.g., signiﬁcant symptom improvement compared with con-

trols; 70% of participants in the treatment group not meeting

the diagnostic criteria for PTSD following the neurofeed-

back treatment (“EEG-Neurofeedback as a Tool to Modu-

late Cognition and Behavior: A Review Tutorial,” Frontiers

in Human Neuroscience, Stefanie Enriquez-Geppert, Rene

J. Huster, Christoph S. Herrmann, 2017). There were also

notable improvements in growth areas, suggesting tangible

beneﬁts to participants.

For example, one subject reported improvement in qual-

ity of time with family and children: “I noticed that on the

days with neurofeedback secession to have better interaction

with my family. For example, last week I was calmer with

my children and quality time was much better. My short fuse

was longer.”

Another participant reported control over self-regulation

of negative thoughts: “Neurofeedback made me more con-

scious of my internal feelings and provided me with more

self-control. This allows me to put my head in the right

space. Also, it helps me relax faster.”

However, not all participants directly beneﬁted from this

study. One participant reported the following: “Neurofeed-

back was a good opportunity to be mindful. Disconnecting

and meditative aspect was helpful. However, I noted no spe-

ciﬁc changes in my selected growth areas. I still have trouble

focusing on the task, my mind wonders a lot and I have a

hard time falling asleep.”

This exploratory study has several limitations. First, the

results are based on one experiment with few subjects and

without a “sham” neurofeedback control group. Given the

extensive training needed in the study (eight neurofeedback

The 6 types of brain waves

Brain wave speed is measured in hertz (cycles per second) and

divided into bands delineating slow, moderate and fast waves.

Here are descriptions for the different types of brain waves:

Infra-low (below 0.5 Hz). Infra-low brain waves, also

known as slow cortical potentials, are thought to be the basic

cortical rhythms that underlie higher brain functions. Their slow

nature makes them difﬁcult to detect and accurately measure.

Delta (0.5 to 3 Hz). Deltas are slow, loud brain waves,

low frequency and deeply penetrating like a drum beat. They

are generated in deepest meditation and dreamless sleep. Delta

waves suspend external awareness and are the source of empathy.

Healing and regeneration are stimulated in this state.

Theta (3 to 8 Hz). Thetas occur in sleep and are also

dominant in deep meditation. They are the gateway to learning,

memory and intuition. It is that twilight state experienced as we

wake or drift off to sleep.

Alpha (8 to 12 Hz). Alpha brainwaves are dominant during

quietly ﬂowing thoughts, and in some meditative states. Alpha is

the resting state for the brain and aids overall mental coordination,

calmness, alertness, mind/body integration and learning.

Beta (12 to 38 Hz). Beta brainwaves are present in our

normal waking state of consciousness when attention is directed

toward cognitive tasks and the outside world. Beta is a present

when we are alert, attentive, engaged in problem-solving,

judgment, decision-making or focused mental activity. Beta waves

are further divided into three bands: Lo-Beta or Beta 1 (12-15 Hz),

a “fast idle;” Beta 2 (15-22 Hz) high engagement; and Hi-Beta or

Beta3 (22-38 Hz) highly complex thought, anxiety or excitement.

Gamma waves (38 to 42 Hz). The fastest of brain waves

relate to simultaneous processing of information from different

brain areas. Gamma waves pass information rapidly and quietly.

Source: brainworksneurotherapy.com

September 2019 | ISE Magazine 37

sessions, 35 minutes each in length over an eight-week pe-

riod), only a modest number of participants could receive the

treatment. Such is often common in these types of studies.

Second, nonrandom allocation of subjects led to an inten-

tional higher level of initial burnout-depression in the treat-

ment group, thus raising the possibility that they had a larger

potential to improve.

Nevertheless, the signiﬁcant improvement in cognitive

workload following the neurofeedback treatment suggests

that this innovative approach warrants further evaluation as

a potential intervention to address burnout-depression con-

cerns for surgery residents.

Future studies could also examine the degree to which

burnout and depression symptoms in surgical residents cor-

relate with speciﬁc alterations in EEG or other neural activa-

tion patterns, as well as behavior outcomes. 

This study was supported by funding from the University of North

Carolina Health Care System. The authors thank David Planting

for his assistance in administering neurofeedback sessions and research

participants for their time and effort.

Lukasz M. Mazur, Ph.D., is an associate professor and a director

of the Healthcare Engineering Division at the Radiation Oncology

Department in the UNC School of Medicine at Chapel Hill, North

Carolina. He earned his bachelor’s degree, master’s degree and Ph.D.

in industrial and management engineering from Montana State Uni-

versity. His research interests include engineering management as it

pertains to continuous quality and patient safety efforts in healthcare

and human factors engineering with a focus on workload and indi-

vidual performance during human-machine interactions.

Alana Campbell, Ph.D., is an assistant professor in the Department

of Psychiatry at the University of North Carolina at Chapel Hill,

North Carolina. She earned her bachelor’s degree in human develop-

ment and family studies from Cornell University, a master’s degree in

psychology from Colorado State University and a Ph.D. in psychol-

ogy and cognitive neuroscience from Colorado State University.

Prithima Mosaly, Ph.D., is a research assistant professor in the De-

partment of Radiation and Oncology at the University of North

Carolina at Chapel Hill, North Carolina. She earned her master’s

degree in health policy and management from UNC-Chapel Hill

and Ph.D. in industrial and manufacturing engineering at the Uni-

versity of Wisconsin Milwaukee and is a certiﬁed Human Factors

and Ergonomics Professional.

Karthik Adapa, M.D., is a graduate research assistant for the Caro-

lina Health Informatics Program at the University of North Carolina

at Chapel Hill, North Carolina.

Ian Kratzke, M.D., practices in the Department of Surgery at the

University of North Carolina, Chapel Hill, North Carolina. He

earned his M.D. from the Medical School: State University of New

York.

Lawrence M. Marks, M.D., is a radiation oncologist at the Univer-

sity of North Carolina, Chapel Hill, North Carolina. He received

his medical degree from University of Rochester School of Medicine

and Dentistry and has been in practice for more than 20 years. He is

chair and Dr. Sidney K. Simon Distinguished Professor of Oncol-

ogy Research.

Samantha Meltzer-Brody, M.D., is an associate professor at the

University of North Carolina, Chapel Hill, North Carolina. She is

the Ray M. Hayworth and Family Distinguished Professor of Mood

and Anxiety Disorders; executive medical director of the UNC Well-

Being Program; and director of the Perinatal Psychiatry Program,

UNC Center for Women’s Mood Disorders. She earned her bach-

elor’s degree in biology and psychology from Simmons College and

her M.D. from Northwestern University Medical School.

Timothy M. Farrell, M.D., is professor of surgery and director of

minimally invasive surgery at the University of North Carolina,

Chapel Hill, North Carolina. He earned his M.D. from the Uni-

versity of Medicine and Dentistry of New Jersey.